Going fast or going green? Evidence from environmental speed limits in Norway

This paper studies the impact of speed limits on local air pollution using a series of date-specific speed limit reductions in Oslo over the 2004–2011 period. We find that lowering the speed limit from 80 to 60 km/h reduces travel speed by 5.8 km/h. However, we find no evidence of reduced air pollution as measured next to the treated roads. Our estimates suggest an annual time loss of the speed limit reductions of 66 USD per affected vehicle. Our findings imply that policy makers need to consider other actions than speed limit reductions to improve local air quality.     

Forecasting journey time distribution with consideration to abnormal traffic conditions

Travel time is an important index for managers to evaluate the performance of transportation systems and an intuitive measure for travelers to choose routes and departure times. An important part of the literature focuses on predicting instantaneous travel time under recurrent traffic conditions to disseminate traffic information. However, accurate travel time prediction is important for assessing the effects of abnormal traffic conditions and helping travelers make reliable travel decisions under such conditions. This study proposes an online travel time prediction model with emphasis on capturing the effects of anomalies. The model divides a path into short links. A Functional Principal Component Analysis (FPCA) framework is adopted to forecast link travel times based on historical data and real-time measurements. Furthermore, a probabilistic nested delay operator is used to calculate path travel time distributions. To ensure that the algorithm is fast enough for online applications, parallel computation architecture is introduced to overcome the computational burden of the FPCA. Finally, a rolling horizon structure is applied to online travel time prediction. Empirical results for Guangzhou Airport Expressway indicate that the proposed method can capture an abrupt change in traffic state and provide a promising and reliable travel time prediction at both the link and path levels. In the case where the original FPCA is modified for parallelization, accuracy and computational effort are evaluated and compared with those of the sequential algorithm. The proposed algorithm is found to require only a piece rather than a large set of traffic incident records.     

Probe vehicle data sampled by time or space: Consistent travel time allocation and estimation

A characteristic of low frequency probe vehicle data is that vehicles traverse multiple network components (e.g., links) between consecutive position samplings, creating challenges for (i) the allocation of the measured travel time to the traversed components, and (ii) the consistent estimation of component travel time distribution parameters. This paper shows that the solution to these problems depends on whether sampling is based on time (e.g., one report every minute) or space (e.g., one every 500 m). For the special case of segments with uniform space-mean speeds, explicit formulae are derived under both sampling principles for the likelihood of the measurements and the allocation of travel time. It is shown that time-based sampling is biased towards measurements where a disproportionally long time is spent on the last segment. Numerical experiments show that an incorrect likelihood formulation can lead to significantly biased parameter estimates depending on the shapes of the travel time distributions. The analysis reveals that the sampling protocol needs to be considered in travel time estimation using probe vehicle data.     

Predicting travel time reliability using mobile phone GPS data

Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. Many of these uses (such as those for mapping services like Bing or Google Maps) require predictions for routes in the road network, at arbitrary times; the highest-volume source of data for this purpose is GPS data from mobile phones. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones or other probe vehicles. TRIP captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and is computationally efficient, even for very large road networks and datasets. We apply TRIP to predict travel time on the road network of the Seattle metropolitan region, based on large volumes of GPS data from Windows phones. TRIP provides improved interval predictions (forecast ranges for travel time) relative to Microsoft’s engine for travel time prediction as used in Bing Maps. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables, and differing from the observed travel times by only 10.1% on average over 35,190 test trips. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks.     

增强Iinux环境中PHP功能的方法

针对PHP没有提供用户所要求的满足他们需要的特殊的功能，作者介绍了3种方法将由用户自由编制的函数加入PHP中，大大增强了PHP的功能。     

客车行车安全监测系统中走行部节点功能校验系统的实现

为了对客车行车安全监测系统中车辆走行部节点功能进行校验 ,将以往典型试验的采样数据无失真的回放出来 ,代替车辆走行部节点的采样信号源 (传感器拾取的信号 ) ,车辆走行部节点装置对输出的波形进行采样、预处理和数据分析 ,方便实用     

动力分散型电动车组车载微机网络的功能划分与接口设想

从车载微机网络的拓扑结构、网络组成单元及功能划分、接口与控制逻辑、故障处理几个方面提出了动力分散型电动车组车载微机网络的功能划分与接口设想。     

城市轻轨车辆最佳运行控制方案的研究

本文给出一种利用微型机实现城市轨道车辆最佳运行的控制方案,即在变化的外界条件下确定断电和制动时刻,以指导司机驾驶车辆,实现能耗最小和准时运行.该控制方案的特点是不断地利用实测值并结合估算确定断电和制动时刻,简单易行.     

FAULT IDENTIFICATION IN HETEROGENEOUS NETWORKS USING TIME SERIES ANALYSIS

Fault management is crucial to provide quality of service grantees for the future networks, and fault identification is an essential part of it. A novel fault identification algorithm is proposed in this paper, which focuses on the anomaly detection of network traffic. Since the fault identification has been achieved using statistical information in management information base, the algorithm is compatible with the existing simple network management protocol framework. The network traffic time series is verified to be non-stationary. By fitting the adaptive autoregressive model, the series is transformed into a multidimensional vector. The training samples and identifiers are acquired from the network simulation. A k-nearest neighbor classifier identifies the system faults after being trained. The experiment results are consistent with the given fault scenarios, Which prove the accuracy of the algorithm. The identification errors are discussed to illustrate that the novel fault identification algorithm is adaptive in the fault scenarios with network traffic change.     

SYNTHESIS OF THERMAL RESPONSE FACTORS AND Z-TRANSFER FUNCTION COEFFICIENTS FOR CALCULATION OF ROOM TEMPERATURE VARIATION

1IntroductionTheresearchesonthecalculatingapproachtodynamicair-conditioningloadshaveledtoatechnicalrevolutioninthefieldofair-condition-ing.Themethodssuchasthethermalresponsefactormethod(TRFM)andthez-transferfunc-tioncoefficientmethod(ZTFM)areknownbymoreandmoreresearchersandtechnicians.Thedynamicmethodscanbetterreflectthecharac-teristicsofpracticalobjectsthanthesteadymethods,andsomeachievementshavebeenmadeinthedynamiccalculationfield,buttherearestillmanyproblemsthatmustbesolved.Uptonow,ther…